OBJECTIVES: Precision medicine is an emerging approach, under which treatments are optimised after accounting for individuals’ genotypic, phenotypic, and/or environmental characteristics. We assess the economic value of precision medicine, using an example of patients with asthma who are receiving GINA (Global Initiative for Asthma) step 2 treatment in the US.

METHODS: A Markov model was developed to estimate the costs and benefits across the natural history of asthma, represented by five GINA steps. In the model, patients could step up/down the treatment, experience an exacerbation, remit or die. A new treatment X (Tx) could expedite step-downs, delay step-ups, and reduce exacerbations among patients with a specific genotype (GT). Those who tested positive for GT will receive Tx, and the rest, standard of care (SoC). To assess the value of Tx when compared to using SoC alone, its efficacy, the sensitivity/specificity of GT testing, and GT prevalence in patients were varied to estimate the cost-effective price of Tx (at a willingness-to-pay threshold of $50,000 per a quality-adjusted life year [QALY]). Parameters were estimated from the literature. Costs and QALYs were discounted at 3.0% per year.

RESULTS: The cost-effective price of Tx was higher when GT was more prevalent and the diagnostic test more precise. Following a cohort of asthma patients, at an efficacy level of 50%, the monthly cost-effective price of Tx increased from $170 to $405, if the prevalence of GT and the sensitivity/specificity of GT testing both increased from 25% to 75%. The cost-effective price was most sensitive at high prevalence/low precision, and low prevalence/high precision of the genotype/its diagnostic test respectively.

CONCLUSIONS: The economic value of precision medicine depends not only on the treatment efficacy, but also the prevalence of the genotypes/phenotypes of interest, and in our example, even more so, on the accurate identification of eligible patients.